scholarly journals Extraction of Rice Heavy Metal Stress Signal Features Based on Long Time Series Leaf Area Index Data Using Ensemble Empirical Mode Decomposition

2017 ◽  
Vol 14 (9) ◽  
pp. 1018 ◽  
Author(s):  
◽  
◽  
◽  
◽  
Keyword(s):  
Heavy Metal ◽  
Time Series ◽  
Leaf Area Index ◽  
Heavy Metal Stress ◽  
Ensemble Empirical Mode Decomposition ◽  
Area Index ◽  
Signal Features ◽  
Mode Decomposition ◽  
Stress Signal ◽  
Long Time

scholarly journals Spatio-temporal Index Based on Time Series of Leaf Area Index for Identifying Heavy Metal Stress in Rice under Complex Stressors

2020 ◽  
Vol 17 (7) ◽  
pp. 2265
Author(s):  
Yibo Tang ◽  
Meiling Liu ◽  
Xiangnan Liu ◽  
Ling Wu ◽  
Bingyu Zhao ◽  
...  
Keyword(s):  
Heavy Metal ◽  
Time Series ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Heavy Metal Stress ◽  
Metal Stress ◽  
Growth Stages ◽  
Area Index ◽  
Temporal Features ◽  
Stress Levels

Crops under various types of stresses, such as stress caused by heavy metals, drought and pest/disease exhibit similar changes in physiological-biochemical parameters (e.g., leaf area index [LAI] and chlorophyll). Thus, differentiating between heavy metal stress and nonheavy metal stress presents a great challenge. However, different stressors in crops do cause variations in spatiotemporal characteristics. This study aims to develop a spatiotemporal index based on LAI time series to identify heavy metal stress under complex stressors on a regional scale. The experimental area is located in Zhuzhou City, Hunan Province. The situ measured data and Sentinel-2A images from 2017 and 2018 were collected. First, a series of LAI in rice growth stages was simulated based on the WOrld FOod STudies (WOFOST) model incorporated with Sentinel 2 images. Second, the local Moran’s I and dynamic time warping (DTW) of LAI were calculated. Third, a stress index based on spatial and temporal features (SIST) was established to assess heavy metal stress levels according to the spatial autocorrelation and temporal dissimilarity of LAI. Results revealed the following: (1) The DTW of LAI is a good indicator for distinguishing stress levels. Specifically, rice subjected to high stress levels exhibits high DTW values. (2) Rice under heavy metal stress is well correlated with high-high SIST clusters. (3) Rice plants subjected to high pollution are observed in the northwest of the study regions and rice under low heavy metal stress is found in the south. The results suggest that SIST based on a sensitive indicator of rice biochemical impairment can be used to accurately detect regional heavy metal stress in rice. Combining spatial-temporal features and spectral information appears to be a highly promising method for discriminating heavy metal stress from complex stressors.

Long-Time-Series Global Land Surface Satellite Leaf Area Index Product Derived From MODIS and AVHRR Surface Reflectance

2016 ◽  
Vol 54 (9) ◽  
pp. 5301-5318 ◽  
Author(s):  
Zhiqiang Xiao ◽  
Shunlin Liang ◽  
Jindi Wang ◽  
Yang Xiang ◽  
Xiang Zhao ◽  
...  
Keyword(s):  
Time Series ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Land Surface ◽  
Surface Reflectance ◽  
Area Index ◽  
Long Time Series ◽  
Global Land ◽  
Long Time

Improving the Consistency and Continuity of MODIS 8 Day Leaf Area Index Products

2012 ◽  
Vol 58 (2) ◽  
pp. 141-146 ◽  
Author(s):  
Sivasathivel Kandasamy ◽  
Philippe Neveux ◽  
Aleixandre Verger ◽  
Samuel Buis ◽  
Marie Weiss ◽  
...  
Keyword(s):  
Time Series ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Singular Spectrum Analysis ◽  
Area Index ◽  
Standard Methods ◽  
Error Metrics ◽  
Mode Decomposition ◽  
Low Pass ◽  
Global Vegetation

Improving the Consistency and Continuity of MODIS 8 Day Leaf Area Index Products Time Series Analysis of Leaf Area Index (LAI) is vital to the understanding of global vegetation dynamics. The LAI time series derived from satellite observations are usually not complete and noisy due to cloud contamination and uncertainties in the retrieval techniques. In this paper, the continuity and consistency of the MODIS 8 day LAI products are improved using a method based on Caterpillar Singular Spectrum Analysis. The proposed method is compared with other standard methods: Savitzky-Golay filter, Empirical Mode Decomposition, Low Pass filtering and Asymmetric Gaussian fitting. The experiment demonstrates the smoothing and gapfilling ability of the developed method, which is more robust across the biomes both in terms of root mean square error metrics and bias metrics as compared to the standard methods.

Evaluation of four long time-series global leaf area index products

2017 ◽  
Vol 246 ◽  
pp. 218-230 ◽  
Author(s):  
Zhiqiang Xiao ◽  
Shunlin Liang ◽  
Bo Jiang
Keyword(s):  
Time Series ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Area Index ◽  
Long Time Series ◽  
Long Time

scholarly journals Analogy-Based Crop Yield Forecasts Based on Temporal Similarity of Leaf Area Index

2021 ◽  
Vol 13 (16) ◽  
pp. 3069
Author(s):  
Yadong Liu ◽  
Junhwan Kim ◽  
David H. Fleisher ◽  
Kwang Soo Kim
Keyword(s):  
Time Series ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Crop Yield ◽  
Satellite Data ◽  
Growing Season ◽  
Environmental Data ◽  
Area Index ◽  
Current Season ◽  
Wide Range

Seasonal forecasts of crop yield are important components for agricultural policy decisions and farmer planning. A wide range of input data are often needed to forecast crop yield in a region where sophisticated approaches such as machine learning and process-based models are used. This requires considerable effort for data preparation in addition to identifying data sources. Here, we propose a simpler approach called the Analogy Based Crop-yield (ABC) forecast scheme to make timely and accurate prediction of regional crop yield using a minimum set of inputs. In the ABC method, a growing season from a prior long-term period, e.g., 10 years, is first identified as analogous to the current season by the use of a similarity index based on the time series leaf area index (LAI) patterns. Crop yield in the given growing season is then forecasted using the weighted yield average reported in the analogous seasons for the area of interest. The ABC approach was used to predict corn and soybean yields in the Midwestern U.S. at the county level for the period of 2017–2019. The MOD15A2H, which is a satellite data product for LAI, was used to compile inputs. The mean absolute percentage error (MAPE) of crop yield forecasts was <10% for corn and soybean in each growing season when the time series of LAI from the day of year 89 to 209 was used as inputs to the ABC approach. The prediction error for the ABC approach was comparable to results from a deep neural network model that relied on soil and weather data as well as satellite data in a previous study. These results indicate that the ABC approach allowed for crop yield forecast with a lead-time of at least two months before harvest. In particular, the ABC scheme would be useful for regions where crop yield forecasts are limited by availability of reliable environmental data.

scholarly journals Ensemble Empirical Mode Decomposition with Adaptive Noise with Convolution Based Gated Recurrent Neural Network: A New Deep Learning Model for South Asian High Intensity Forecasting

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 931
Author(s):  
Kecheng Peng ◽  
Xiaoqun Cao ◽  
Bainian Liu ◽  
Yanan Guo ◽  
Wenlong Tian
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Deep Learning ◽  
South Asian ◽  
Recurrent Neural Network ◽  
Ensemble Empirical Mode Decomposition ◽  
South Asian High ◽  
Mode Decomposition ◽  
Adaptive Noise ◽  
Deep Learning Model

The intensity variation of the South Asian high (SAH) plays an important role in the formation and extinction of many kinds of mesoscale systems, including tropical cyclones, southwest vortices in the Asian summer monsoon (ASM) region, and the precipitation in the whole Asia Europe region, and the SAH has a vortex symmetrical structure; its dynamic field also has the symmetry form. Not enough previous studies focus on the variation of SAH daily intensity. The purpose of this study is to establish a day-to-day prediction model of the SAH intensity, which can accurately predict not only the interannual variation but also the day-to-day variation of the SAH. Focusing on the summer period when the SAH is the strongest, this paper selects the geopotential height data between 1948 and 2020 from NCEP to construct the SAH intensity datasets. Compared with the classical deep learning methods of various kinds of efficient time series prediction model, we ultimately combine the Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method, which has the ability to deal with the nonlinear and unstable single system, with the Permutation Entropy (PE) method, which can extract the SAH intensity feature of IMF decomposed by CEEMDAN, and the Convolution-based Gated Recurrent Neural Network (ConvGRU) model is used to train, test, and predict the intensity of the SAH. The prediction results show that the combination of CEEMDAN and ConvGRU can have a higher accuracy and more stable prediction ability than the traditional deep learning model. After removing the redundant features in the time series, the prediction accuracy of the SAH intensity is higher than that of the classical model, which proves that the method has good applicability for the prediction of nonlinear systems in the atmosphere.

Spatiotemporal Trend and Variability of Precipitation in Taiwan Based on Multi-dimensional Ensemble Empirical Mode Decomposition (MEEMD)

2021 ◽  
Author(s):  
Chun-Hsiang Tang ◽  
Christina W. Tsai
Keyword(s):  
Time Series ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Short Term ◽  
Time Frequency ◽  
Spatial Locality ◽  
Temperature And Precipitation ◽  
Mode Decomposition ◽  
Climatic Scenarios

&lt;p&gt;Abstract&lt;/p&gt;&lt;p&gt;Most of the time series in nature are nonlinear and nonstationary affected by climate change particularly. It is inevitable that Taiwan has also experienced frequent drought events in recent years. However, drought events are natural disasters with no clear warnings and their influences are cumulative. The difficulty of detecting and analyzing the drought phenomenon remains. To deal with the above-mentioned problem, Multi-dimensional Ensemble Empirical Mode Decomposition (MEEMD) is introduced to analyze the temperature and rainfall data from 1975~2018 in this study, which is a powerful method developed for the time-frequency analysis of nonlinear, nonstationary time series. This method can not only analyze the spatial locality and temporal locality of signals but also decompose the multiple-dimensional time series into several Intrinsic Mode Functions (IMFs). By the set of IMFs, the meaningful instantaneous frequency and the trend of the signals can be observed. Considering stochastic and deterministic influences, to enhance the accuracy this study also reconstruct IMFs into two components, stochastic and deterministic, by the coefficient of auto-correlation.&lt;/p&gt;&lt;p&gt;In this study, the influences of temperature and precipitation on the drought events will be discussed. Furthermore, to decrease the significant impact of drought events, this study also attempts to forecast the occurrences of drought events in the short-term via the Artificial Neural Network technique. And, based on the CMIP5 model, this study also investigates the trend and variability of drought events and warming in different climatic scenarios.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;Keywords: Multi-dimensional Ensemble Empirical Mode Decomposition (MEEMD), Intrinsic Mode Function(IMF), Drought&lt;/p&gt;

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